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作 者:Shuang-Peng Sun Shi-Hong Liao Qi Guo Qiao Wang Liang Gao
机构地区:[1]Key Laboratory of Computational Astrophysics,National Astronomical Observatories,Chinese Academy of Sciences,Beijing 100101,China [2]University of Chinese Academy of Sciences,Beijing 100049,China [3]Institute for Computational Cosmology,Department of Physics,Durham University,Science Laboratories,South Road,Durham DH13LE,England
出 处:《Research in Astronomy and Astrophysics》2020年第4期19-30,共12页天文和天体物理学研究(英文版)
基 金:support from the National Key Program for Science and Technology Research and Development(2017YFB0203300);support from the National Natural Science Foundation of China(NSFC)(No.11425312);two Royal Society Newton Advanced Fellowships;the hospitality of the Institute for Computational Cosmology at Durham University;supported by NSFC(Grant Nos.11573033 and 11622325);the“Recruitment Program of Global Youth Experts”of China;the NAOC grant(Y434011V01)。
摘 要:We introduce a new halo/subhalo finder,HIKER(a Halo fInder based on KERnel-shift algorithm),which takes advantage of a machine learning method–the mean-shift algorithm combined with the Plummer kernel function,to effectively locate density peaks corresponding to halos/subhalos in density field.Based on these density peaks,dark matter halos are identified as spherical overdensity structures,and subhalos are bound substructures with boundaries at their tidal radius.By testing HIKER code with mock halos,we show that HIKER performs excellently in recovering input halo properties.In particular,HIKER has higher accuracy in locating halo/subhalo centres than most halo finders.With cosmological simulations,we further show that HIKER reproduces the abundance of dark matter halos and subhalos quite accurately,and the HIKER halo/subhalo mass functions and Vmax functions are in good agreement with two widely used halo finders,SUBFIND and AHF.
关 键 词:methods:N-body simulations galaxies:halos galaxies:evolution cosmology:theory DARK matter
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